Autonomous Vehicle Obstacle Avoiding and Goal Position Reaching by Behavioral Cloning

Author

Kulic, Ranka ; Vukic, Zoran

Author_Institution

Fac. of Maritime of Studies

fYear

2006

fDate

6-10 Nov. 2006

Firstpage

3939

Lastpage

3944

Abstract

The problem of dynamic path generation for the autonomous vehicle in environments with unmoving obstacles is presented. Generally, the problem is known in the literature as the vehicle motion planning. In this paper the behavioural cloning approach is applied to design the vehicle controller. In behavioural cloning, the system learns from control traces of a human operator. To learn from control traces the machine learning algorithm and neural network algorithms are used. The goal is to find the controller for the autonomous vehicle motion planning in situation with infinite number of obstacles

Keywords

collision avoidance; control system synthesis; learning (artificial intelligence); mobile robots; neurocontrollers; remotely operated vehicles; autonomous vehicle obstacle avoidance; behavioural cloning approach; dynamic path generation; goal position; machine learning algorithm; neural network algorithms; vehicle controller design; vehicle motion planning; Cloning; Control systems; Humans; Machine learning algorithms; Mobile robots; Motion control; Remotely operated vehicles; Space vehicles; Testing; Vehicle dynamics;

fLanguage

English

Publisher

ieee

Conference_Titel

IEEE Industrial Electronics, IECON 2006 - 32nd Annual Conference on

Conference_Location

Paris

ISSN

1553-572X

Print_ISBN

1-4244-0390-1

Type

conf

DOI

10.1109/IECON.2006.347628

Filename

4153054